PRADOT learns local and global prototypes via optimal transport with a fused feature-spatial cost, achieving competitive anomaly detection and localization on industrial benchmarks.
In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
PRADOT learns local and global prototypes via optimal transport with a fused feature-spatial cost, achieving competitive anomaly detection and localization on industrial benchmarks.